查看更多>>摘要：Background and Aims: Patients with hepatocellular carci-noma (HCC) surgically resected are at risk of recurrence;however, the risk factors of recurrence remain poorly un-derstood. This study intended to establish a novel machine learning model based on clinical data for predicting early re-currence of HCC after resection. Methods: A total of 220 HCC patients who underwent resection were enrolled. Clas-sification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and preci-sion of the model were used to assess the model's accura-cy and identify efficiency of the model. Results: Recurrent HCC developed in 89 (40.45％) patients at a median time of 14 months from primary resection. In principal compo-nent analysis, tumor size, tumor grade differentiation, por-tal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-Ⅱ(PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After compar-ing different machine learning methods, including logistic re-gression, decision tree, na?ve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, preci-sion of the K-NN model were 70.6％, 51.9％, 70.1％, respec-tively. The standard deviation was 0.020. Conclusions: The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.
NETL
NSTL
万方数据
维普